| D10-1068 | the grammar using a supervised | parse selection | model . In the standard process |
| C02-2025 | building statistical models of | parse selection | are possible . At the simplest |
| C02-2025 | Table 2 shows the accuracy of | parse selection | using the models described above |
| D10-1068 | standard process for creating a | parse selection | model is : 1 . parse the training |
| D09-1135 | is proved to be effective for | parse selection | . Recently , Agirre et al. ( |
| D10-1068 | styles of parsing , however , | parse selection | is based on a statistical model |
| D10-1067 | algorithms based on quality-based | parse selection | . We first detail a basic version |
| D10-1068 | is generally streamlined with | parse selection | models , creating the initial |
| C02-2025 | between the parsing problem and the | parse selection | problem . The first column of |
| D10-1068 | development of fully unsupervised | parse selection | models . The particular style |
| D09-1135 | data to train a discriminative | parse selection | model combining syntactic features |
| D10-1068 | completely removing this requirement of | parse selection | on explicitly treebanked data |
| D10-1068 | effort to produce the treebank , | parse selection | is not possible . Furthermore |
| D10-1068 | In the standard process , the | parse selection | model is trained over a hand-disambiguated |
| C02-2025 | they assign to them . We report | parse selection | performance as percentage of |
| C02-2025 | tag sel . parse sel . tag and | parse selection | tasks ( accuracy ) . The results |
| C02-2025 | compare this work with other work on | parse selection | for unificationbased grammars |
| C02-2025 | grammar , that is , for which the | parse selection | task is nontrivial . ) We examine |
| C00-1029 | which can be used \ -LSB- ` or | parse selection | . However , knowledge of se - |
| D10-1068 | unsupervised . <title> Unsupervised | Parse Selection | for HPSG </title> Dridan Abstract |
